BBC English with an Accent: “African” and “Asian” Accents and the Translation of Culture in British Broadcasting
Bibliographic record
Abstract
Foreign accents acted by Anglophone actors are a ubiquitous but politically and theoretically problematic feature of many audiovisual productions in the English-speaking world. This paper investigates the use of Tswana and Japanese accents in two BBC productions as acts of audiovisual translation (AVT) which are illustrative of a more general problematic of Western representations of non-Western languages and cultures. It argues that the phonological features of speech, which are classified as accents, divide the community of native speakers into different social groups and that they create and maintain boundaries between native and non-native speakers. Language discrimination is recognised by the BBC as a problem in relation to its domestic audience and the Corporation actively attempts to become more inclusive and representative of British society by broadcasting non-standard accents. On the other hand, when representing foreign, and especially post-colonial and non-Western languages and cultures, accent is used to define the boundary between the native English-speaking community and its outside. Accents are used to represent and translate the outside in stereotyping ways that tend towards racialisation and towards actors using generic “Southern African” and “East Asian” accents that bear little resemblance to the actual phonological profile of native speakers of Tswana and Japanese.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".